UAV Security Technology for Ultra-Ultra High Voltage Substations, Converter Stations, and Transmission Lines Based on Adaptive Extended Kalman Filtering and Multi-source Information Fusion

The risks of unauthorized UA V intrusions into ultra-ultra high voltage facilities are becoming increasingly prominent. Malicious interference or sabotage could lead to power outages, threatening energy security and social stability. Traditional security measures suffer from large monitoring blind spots, high false alarm rates, and weak recognition capabilities, failing to meet the needs of precise monitoring. However, the multi-source fusion of spectrum, radar, and photoelectric detection technologies provides a new approach for UAV security. Addressing the security requirements of ultra-ultra high voltage facilities, this paper studies the fusion sensing architecture and data fusion, offering a systematic solution for UAV prevention in power facilities and improving the security protection level of critical infrastructure. Through improved Kalman filtering [1] in multi-source information fusion [2–4], optimal estimation of dynamic target states can be achieved. By establishing a state-space model, time-series processing is performed on measurement data from spectrum, radar, photoelectric, and other sensors. A recursive algorithm fuses predicted values with measured values, effectively filtering out noise and correcting sensor biases. In the security of ultra-ultra high voltage facilities, this technology enhances the trajectory continuity and positioning accuracy of UAV targets [5], reduces the impact of asynchronous errors in multi-source data, provides reliable state inputs for subsequent intelligent decision-making, and strengthens the robustness of monitoring systems in complex environments.